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https://github.com/oobabooga/text-generation-webui.git
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39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
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'''
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Converts a transformers model to .pt, which is faster to load.
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Run with python convert.py /path/to/model/
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Make sure to write /path/to/model/ with a trailing / and not
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/path/to/model
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Output will be written to torch-dumps/name-of-the-model.pt
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'''
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from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, AutoTokenizer, set_seed
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from transformers import GPT2Tokenizer, GPT2Model, T5Tokenizer, T5ForConditionalGeneration
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import torch
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import sys
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from sys import argv
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import time
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import glob
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import psutil
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print(f"torch-dumps/{argv[1].split('/')[-2]}.pt")
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if argv[1].endswith('pt'):
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model = OPTForCausalLM.from_pretrained(argv[1], device_map="auto")
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torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
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elif 'galactica' in argv[1].lower():
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model = OPTForCausalLM.from_pretrained(argv[1], low_cpu_mem_usage=True, torch_dtype=torch.float16)
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#model = OPTForCausalLM.from_pretrained(argv[1], low_cpu_mem_usage=True, load_in_8bit=True)
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torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
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elif 'flan-t5' in argv[1].lower():
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model = T5ForConditionalGeneration.from_pretrained(argv[1], low_cpu_mem_usage=True, torch_dtype=torch.float16)
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torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
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else:
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print("Loading the model")
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model = AutoModelForCausalLM.from_pretrained(argv[1], low_cpu_mem_usage=True, torch_dtype=torch.float16)
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print("Model loaded")
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#model = AutoModelForCausalLM.from_pretrained(argv[1], device_map='auto', load_in_8bit=True)
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torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
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